A human proteogenomic-cellular framework identifies KIF5A as a modulator of astrocyte process integrity with relevance to ALS

Genome-wide association studies identified several disease-causing mutations in neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). However, the contribution of genetic variants to pathway disturbances and their cell type-specific variations, especially in glia, is poorly understood. We integrated ALS GWAS-linked gene networks with human astrocyte-specific multi-omics datasets to elucidate pathognomonic signatures. It predicts that KIF5A, a motor protein kinesin-1 heavy-chain isoform, previously detected only in neurons, can also potentiate disease pathways in astrocytes. Using postmortem tissue and super-resolution structured illumination microscopy in cell-based perturbation platforms, we provide evidence that KIF5A is present in astrocyte processes and its deficiency disrupts structural integrity and mitochondrial transport. We show that this may underly cytoskeletal and trafficking changes in SOD1 ALS astrocytes characterised by low KIF5A levels, which can be rescued by c-Jun N-terminal Kinase-1 (JNK1), a kinesin transport regulator. Altogether, our pipeline reveals a mechanism controlling astrocyte process integrity, a pre-requisite for synapse maintenance and suggests a targetable loss-of-function in ALS.

A lthough human genome-wide association studies (GWAS) have successfully identified genetic variants associated with the development of neurodegenerative diseases, it is still poorly understood how these variants exert their effects on disease risk. Data from GWAS are typically assessed in a single nucleotide polymorphism (SNP) by SNP manner without the consideration of the joint effects of multiple functionally related genes [1][2][3] . In addition, natural variation in gene expression in individuals may also influence pathway manifestation evoked by single genetic variants 2,4 , and the effects are seldom validated across multiple cell types contributing to pathogenesis 5 . Therefore, to develop more effective therapeutic strategies, we need to understand the relevance of identified associations in various cell types, using better predictive models. In this regard, networkbased expansion of GWAS-linked genes has shown promise in identifying such biological pathways [6][7][8][9] . This is based on projecting GWAS-linked genes onto a human protein-protein interaction (PPI) network, in which interacting partners are ranked as trait-associated genes. Such network propagation strategies have been demonstrated as powerful methods for drug target identification 10 , highlighting the sufficient biologically relevant predictive power of these algorithms.
Elucidating the effects of associated genetic variants and establishing their cell type-specificity is especially relevant in amyotrophic lateral sclerosis (ALS), where there are currently no effective treatments. ALS is a fatal neurodegenerative disease in which cortical and spinal motor neurons and glial cells, such as astrocytes, are primarily affected, resulting in limb and respiratory muscle paralysis 11 . It is still unresolved how astrocytes trigger and propagate pathology. There is growing evidence for both ALS-causing mutation-driven astrocytic toxic gain of function and loss of function 12 , proposing their key involvement in neuronal pathogenesis. Therefore, it is relevant to address how astrocytes are affected by a combination of genetic variants of different frequencies, potentially enhancing their susceptibility to pathogenic pathways. Since observations of astrocytic roles can often be obscured by their adaptive reactive response to neuronal insults 13 , the combination of using complex datasets for unbiased computational predictions and mechanistic validations in culturebased platforms could enhance discovery.
Here we demonstrate a proteogenomic-cell biology pipeline relevant to exploring ALS pathobiology with a focus on astrocytic signatures. First, we exploit a GWAS network propagation approach to define gene modules enriched in genetic drivers, followed by its integration with our published SOD1 ALS astrocytespecific transcriptomic and proteomic datasets 14 . In particular, this indicates a motor protein kinesin-1 heavy-chain isoform 5 A (KIF5A) expression defect, which also overlaps with disease-driving gene modules identified by GWAS, implying its key involvement in ALS pathways. While kinesin-1/KIF5A dysfunction has recently been identified in KIF5A and other ALS-causing mutations 15,16 , its presence and role have not been demonstrated in cell types other than neurons in the central nervous system 17,18 . Thus, to explore the unknown function of KIF5A in astrocytes and its potential role in ALS, we utilised perturbation experiments in combination with cytoskeletal analyses, biochemical assays and super-resolution structured illumination microscopy (SR-SIM) in human induced pluripotent stem cell (iPSC)-derived and mouse astrocyte culture platforms. We show that reduced KIF5A levels negatively affect process formation, mitochondrial trafficking and its potential association with glutamate transporter EAAT2. While low KIF5Aexpressing SOD1 ALS astrocytes mirror this phenotype, it can be reversed by a kinesin regulator. Our work provides a multidimensional framework for a cell type-specific mechanistic discovery process, which could inform therapeutic strategies for ALS and related neurodegenerative disorders.

Results
Network expansion of GWAS-identified genes implicates key biological processes across neurodegenerative diseases. To achieve unbiased predictions of affected biological pathways in neurodegenerative diseases, we developed an analysis framework, based on a network expansion method. First, we compiled a comprehensive human interactome by combining highconfidence data from multiple databases, resulting in 540,421 interactions between 18,055 proteins. Next, using the GWAS catalogue, we extracted all genes mapping close to single nucleotide polymorphisms (SNPs) significantly associated with one or more of nine neurodegenerative diseases, including ALS. We then applied a network-based approach to identify a set of human proteins that are highly connected with risk genes (Fig. 1a). To confirm the value of this method, we defined two gold-standard gene-to-disease association sets. Genes linked to neurodegenerative diseases were obtained from the DISEASE database 19 (http://diseases.jensenlab.org) and segregated into two groups according to the degree of confidence and known targets of drugs from studies extracted from ChEMBL (https://www.ebi. ac.uk/chembl). We benchmarked the predictive power of identifying these genes by their network propagation score, calculated by the area under the receiver operating curve (ROC). The average score was higher than 0.7 in the two gold standard sets (Fig. 1b), indicating a strong predictive power.
To identify protein communities or modules with disease association, we defined highly interconnected proteins by clustering the interaction network. Protein communities enriched for disease genes were identified by network propagation and labelled as disease-associated gene modules. Next, we explored common pathways involved in the selected neurodegenerative diseases by identifying gene overlaps from all significant modules using the Jaccard index (Fig. 1c). Alzheimer's disease, Parkinson's disease, ALS, and frontotemporal dementia were clustered together, suggesting a higher number of genetic commonalities. Huntington's disease, posterior cortical atrophy, corticobasal degeneration, and progressive supranuclear palsy appeared to be unique, although the smaller number of GWAS hits in these cases may have been a confounding factor. The three modules shared across most diseases, including ALS, were linked to protein ubiquitination, receptor-mediated endocytosis including synaptic vesicle transport, and microtubule organisation (Fig. 1d, Supplementary Fig. 1a). These biological entities are represented in the converging pathways of neurodegenerative diseases 20 . Although the individual GWAS-linked genes related to these biological processes do not strictly overlap, they share protein interaction partners, illustrating the value of network propagation in the discovery of common and specific pathogenic routes. Overall, our computational prediction well reflected the previously described commonalities in pathogenic pathways but also pointed out the divergence of specific genes linked to cellular processes in various neurodegenerative diseases. Therefore, our network-based method is well-suited to propose pathways predisposing to neurodegenerative pathogenesis.
Network-based integration of GWAS with astrocyte-specific multi-omics data predicts susceptibility genes. To explore astrocyte-specific aspects of predisposing pathways to ALS pathogenesis, we integrated our GWAS network-based modules with our published transcriptomic and proteomic datasets deriving from human SOD1 ALS patient-specific iPSC-derived astrocytes 14 . For this, we used genes from the GWAS network modules (n = 179), differentially expressed transcripts (n = 2,772) and altered protein levels (n = 175) between SOD1mutant and control ALS astrocytes, including genetically corrected controls. Our network expansion-based analysis, starting with each of the three datasets that identified 320 overlapping genes (Fig. 2a), indicated potential novel molecular links to ALS. To explore shared pathways within these overlaps, we identified two common gene modules across the datasets, which were related to gene ontology terms 'proteins localised in the ER' (Fig. 2b, c) and 'Golgi-vesicle transport' (Supplementary Fig. 2a-c). Those genes in these modules that showed corresponding changes at transcriptional and protein levels were then selected as top candidates with a high potential to influence astrocyte-specific ALS pathways. Amongst these KIF5A, HLA-DPA, RPS4Y1 had low expression and DYNC1I1, LMAN1 showed high expression in human SOD1 ALS astrocytes (Fig. 2d). When mapped on PPInetworks, proteins coded by four of the five top candidate genes were found to interact with proteins involved in intracellular transport (Fig. 2e). While KIF5A dysfunction has been implicated in axonal transport disturbances in ALS 21,22 , its expression and function have been unexplored in astrocytes. Therefore, in our subsequent analyses, we specifically focussed on the role of KIF5A in astrocytes.
Low KIF5A levels result in reduced astrocyte polarity and shorter processes. We examined whether the KIF5A-related disturbances indicated by our integrative multi-omics analysis result in reduced KIF5A protein levels in astrocytes and alterations in their morphology. This objective was supported by a study implicating KIF5A in microtubule maintenance in polarised cells 23 . First, we immunostained human SOD1 ALS and control patient-derived postmortem spinal cord tissue sections for KIF5A and glial fibrillary acidic protein (GFAP) (Fig. 3a-c). Immunohistochemistry in the human spinal cord tissue detected no or only low levels of KIF5A in cells other than large motor neurons in the spinal cord, similar to that found in other studies 18 ( Fig. 3b; Supplementary Fig. 3a; Supplementary Table 1). Although, we demonstrated that KIF5A immunoractivity (IR) was associated with GFAP+ processes in some astrocytes in the postmortem tissue ( Supplementary Fig. 3a), this method was unsuitable for precisely detecting the KIF5A content in functionally important fine astrocytic processes that are GFAP negative 13,24 . These findings reduced the feasibility of establishing a correlation between the astrocyte process KIF5A abundance and cytoskeletal alterations in the SOD1 ALS samples. Astrocytic cytoskeletal changes were indicated by a 1.88-fold increase in GFAP IR in ALS versus control spinal cords (Fig. 3c). Thus, to increase the detection yield for KIF5A IR and to allow mechanistic observations, we used human ( Supplementary Fig. 3b) and mouse astrocyte cultures following the confirmation of KIF5A antibody specificity ( Supplementary Fig. 4a overlapping modules a b c d Fig. 1 A network-based approach predicts common pathway disruptions in neurodegenerative diseases. a Schematic representation of network propagation by mapping GWAS hits onto protein-protein interactions (left), leading to network modules (right). b Method benchmarking using gene-todisease associations extracted from the DISEASE database and drug targets from ChEMBL datasets. Barplots represent the areas under the receiver operator characteristic (ROC) curves (AUCs) for three conditions, ALS, Parkinson's and Alzheimer's diseases. c Heatmap represents gene overlaps between various neurodegenerative disease modules, based on the Jaccard similarity index. Barplots illustrate the gene counts from the initial GWAS input (red) and from the expanded significant modules used for Jaccard index calculations (yellow). d Heatmap showing overlaps between the 10 gene-network modules, shared amongst at least two neurodegenerative diseases (Jaccard index >0.7, yellow colour), and described using Gene Ontology Biological Process (GOBP) annotation (right sided list). Please, also see Suppl. Figure 1.
the aforementioned multi-omics analyses 14 . This confirmed KIF5A protein content in control astrocytes and a significantly lower level in ALS astrocytes (Fig. 3d). Next, we used the dynamic astrocyte culture platform for mechanistic investigations into the role of KIF5A. For this, we used phalloidin, an F-actin stain, to visualise cytoarchitecture and potential shape changes. It helped define the form factor (FF), the ratio of cell perimeter length to area measurements, a sensitive marker of cell shape. High FF values (closer to 1) define rounder cells with shorter processes, and low values (closer to 0) represent cells with longer processes in astrocytes identified by GFAP immunolabelling (Fig. 3e, Supplementary Fig. 4c). At 30 DIV, ALS astrocytes displayed shorter processes, representing a 2.62-fold greater FF value when compared to their healthy control counterparts (Fig. 3e), raising the possibility of the involvement of KIF5A in astrocytic polarity. To elucidate the potential causative relationship between low KIF5A levels and cytoskeletal/process alterations, we utilised a siRNAbased knock-down (KD) strategy in control patient-derived human astrocytes. First, we confirmed the KD effect in KIF5A protein amounts versus their scrambled (scr) siRNA-treated counterparts (Fig. 4a,b), while the levels of KIF5B and KIF5C subunits remained unchanged. Then, human astrocytes identified by GFAP/phalloidin staining were subjected to cell shape analysis. FF values were increased by 1.92-fold for KIF5A siRNA-treated human astrocytes compared to their scr siRNA-treated counterparts, indicating process shortening or reduced arborisation when KIF5A protein levels were diminished (Fig. 4c). Then, to verify that KIF5A deficiency in human SOD1 ALS astrocytes is directly  Fig. 2 Integration of multi-omics datasets predicts KIF5A-related pathway disturbances. a Protein (red), transcript (yellow) and gene (green) counts representing the input elements and the number of elements following network-expansion (grey). Venn diagrams (lower panel) illustrate the number of overlapping elements between the significant proteome, transcriptome and GWAS modules for the initial input (left) and for the network-expanded dataset (right). b Network illustrating the top significant module overlap (nodes). The edge thickness and colours illustrate the number of shared genes between modules as defined by the Jaccard index. c Barplots demonstrate the Gene Ontology Biological Process (GOBP) enrichment results for the genes included in the triple overlap (Fisher test, Benjamini-Hochberg adjusted p value < 0.05). d Scatter plot showing the log2 ratios for the changes in the proteomic and transcriptomic datasets (grey dots), amongst which the red dots illustrate genes overlapping with significant GWAS modules (b). e Shortest path interactome between the overlapping candidate genes and their first-degree network neighbours. The text colours correspond with the number of sets that share the given gene, and the green coloured nodes represent genes associated with intracellular transport function as indicated (Uniprot Keyword annotation). Please, also see Suppl. responsible for the less arborized phenotype, we transfected these cells with a KIF5A-mScarlet expression plasmid and assessed their morphology against non-transfected cells. This rescued the phenotype of SOD1 ALS astrocytes, as indicated by the significantly lower FF values, while it did not induce further process complexity in non-mutant control astrocytes (Fig. 4d). The KD effect on KIF5A protein levels and cytoskeletal changes were also recapitulated in primary cultures of astrocytes derived from wildtype C57BL/6 mice (Fig. 4e, f). These findings revealed the conserved presence and the regulatory role of KIF5A in human and mouse astrocytes, impacting process morphology.
KIF5A knockdown recapitulates the reduced KIF5A distribution, mitochondrial traffic and microtubule organisation observed in SOD1 ALS astrocyte processes. Next, we addressed whether low KIF5A levels can influence kinesin-1/KIF5Adependent traffic along astrocyte processes and whether this has direct consequences for microtubule (MT) arrangements. This objective was supported by the reported effects of kinesin or KIF5A on MT assembly and stability 23,25 . To do so, we analysed KIF5A distribution in processes in ALS astrocytes and in control astrocytes subjected to KIF5A KD. This study was assisted by super-resolution structural illumination microscopy (SR-SIM)based particle analysis approach, a low-throughput but highly suitable method for low abundance protein visualisation ( Fig. 5a-f). This work revealed that KIF5A immunoreactive particle density was significantly reduced in the distal 15 μm segment of α-tubulin+ ALS astrocyte process tips when compared to controls ( Fig. 5a, b). These findings were associated with a reduced presence of mitochondria, a KIF5A cargo 26 , which was established by expressing the ratio of MitoBright (MitoB)-labelled mitochondria and process volumes in control and ALS astrocytes ( Fig. 5c, Supplementary Fig. 5a). To assess the dynamics of this process, we performed live imaging of MitoB+ mitochondria and measured their velocity using the ImageJ TrackMate plugin. This demonstrated a 1.57-fold reduction in mitochondrial velocity calculated from the average values for ALS versus control astrocytes, reflecting a reduction in transport efficacy (Supplementary Fig. 5b-d). The reduced KIF5A+ particle and mitochondrial densities seen in ALS astrocytes were faithfully mimicked by the KIF5A KD studies in control astrocytes ( Fig. 5d-f). Next, we used SR-SIM to examine the effects of low KIF5A abundance on process microarchitecture. Astrocytes were subjected to α-tubulin immunolabelling, followed by directionality analysis (ImageJ v2.0 plugin) of microtubules in a 30 μm segment measured from the tip of GFAP+ processes. The significantly higher dispersion factor values indicated MT disorganisation in human ALS astrocytes when compared to controls, which was mimicked by KIF5A KD in control mouse astrocytes ( Supplementary Fig. 5e, f). Altogether, these findings imply that KIF5A plays a central role in process formation or maintenance and implies its involvement in ALS.  The overexpression of kinesin-1 regulator JNK1 rescues polarity and KIF5A density in SOD1 ALS astrocyte processes.
To examine if low KIF5A expression observed for ALS astrocytes is responsible for its process phenotype, we overexpressed Jun N-terminal kinase-1 (JNK1), a kinesin regulator, by transfecting astrocytes with a JNK1-GFP fusion protein-expressing plasmid. We chose this strategy as the interaction between JNK1 and kinesin-1 is known to promote microtubule growth 23 . Moreover, from a practical perspective, kinases are pharmacologically more targetable than motor proteins or their subunits, such as KIF5A 27,28 . First, we confirmed JNK1 protein expression in SOD1 ALS astrocytes transfected by either the JNK1-GFP or the control GFP plasmid, using immunoblots and fluorescence microscopy. The western blots showed a 2.51-fold increase in JNK1 protein amounts (46 kDa) and exclusive JNK1-GFP fusion protein (75kDA) abundance in JNK1-GFP transfected astrocytes, demonstrated by either JNK1 (Fig. 6a) or GFP immunolabelling ( Supplementary Fig. 6a) and GFPfluorescence in astrocytes (Fig. 6b), while the KIF5A protein levels remained equally low (Fig. 6a). We then compared the shapes of GFP-and JNK1-GFP transfected astrocytes detected by GFP fluorescence. JNK1 overexpression dramatically induced process formation in SOD1 ALS astrocytes, which was marked by a 3.85-fold decrease in FF (Fig. 6b). Next, to address whether improvements in kinesin-1/KIF5A distribution could underlie JNK1 overexpressioninduced process formation, we performed SR- astrocyte cultures. KIF5A and JNK1-GFP IR showed a similar distribution and co-localisation in the process tips (Fig. 6c). Overall, KIF5A particle density was significantly greater along the distal 15 μm segment of α-tubulin+ process tips in the JNK1-GFPtranduced group compared to GFP+ or JNK1-GFP-processes (Fig. 6d). Since KIF5A protein levels remained unaltered, this indicated that JNK1 promotes kinesin-1/KIF5A motility and recruitment into processes of ALS astrocytes.
JNK1 overexpression restores mitochondrial abundance for EAAT2 association in SOD1 ALS astrocyte processes. Next, we addressed whether JNK1 overexpression-induced increase in KIF5A process density has a consequence for mitochondrial availability 26 and potential coupling with the human glutamate transporter EAAT2 in ALS astrocyte processes. This association emerges as an important energy-providing step for astrocyte process plasticity 29 , which helps regulate neuronal homoeostasis. and mitoB-stained mitochondria. b Plots representing KIF5A+ particle densities within 1μm segments along the distal 15 μm length of the process in control and ALS astrocytes (ACs). n = 22 control, 21 SOD1 ALS AC processes; data is expressed as mean ± SEM (grey bands); unpaired two-tailed t-test. c Quantification of mitochondrial density (ratio of mitochondria and process volume) per AC process. n = 21 control and 22 SOD1 ALS AC processes; data is expressed as mean ± SD; two-tailed Mann Whitney test. d Representative SR-SIM immunofluorescence images of control scr siRNA-treated and KIF5A siRNA-treated AC processes, demonstrating α-tubulin-, KIF5A-IR and mitoB-stained mitochondria. e Plots representing KIF5A+ particle densities within 1μm segments along the distal 15μm length of the process in control and ALS ACs. n = 7 control+scr siRNA-treated and 7 SOD1 ALS + KIF5A siRNAtreated AC processes; data expressed as mean ± SEM (grey bands); unpaired two-tailed t-test. f Quantification of mitochondrial density per AC process. n = 8 processes of scr siRNA-treated and KIF5A siRNA-treated AC processes; data represents mean ± SD; two-tailed Mann Whitney test. Scale bars: 4μm (2μm for insets). Please, also see Suppl. For this, we compared the proportion of MitoB-labelled mitochondrial areas overlapping with EAAT2 IR in control and ALS astrocyte processes, which showed a 3.56-fold lower EAAT2/ MitoB area-overlap in the latter (Fig. 7a,b). To examine if this could be attributed to the loss of mitochondrial cargo rather than to lower EAAT2 levels that characterise astrocytes in ALS 30,31 , we measured relative EAAT2 levels compared to controls. This analysis showed only a 35.18% reduction in EAAT2 protein amounts in ALS astrocytes (Fig. 7c). Overall, the more subtle alterations in EAAT2 levels relative to the greater changes in EAAT2/mitochondrial area-overlaps suggest that the KIF5Adependent transport failure may also contribute to EAAT2 dysfunction in ALS.
Finally, to address whether the deficiency of mitochondrial transport may be a result of impaired kinesin-1/KIF5A distribution in SOD1 ALS processes, we repeated the measurements in astrocytes expressing JNK1-GFP (Fig. 7d). We found a 2.79-fold increase in the proportion of EAAT2/MitoB area overlaps in processes of JNK1-GFP-expressing ALS astrocytes versus their non-transfected GFP-counterparts (Fig. 7e). Having established by western blotting that overall neither mitochondrial cyclooxygenase (COX) IV nor EAAT2 protein levels are altered upon the transfection by the JNK1-GFP plasmid (Fig. 7f), our data suggest that JNK1-overexpression improves the transport of mitochondria into the processes and their potential association with EAAT2. Overall, our findings indicate that despite the low KIF5A levels, process structure and mitochondrial availability can be rescued by JNK1 in distal parts of SOD1 ALS astrocyte processes. These results corroborate the likely involvement of KIF5A-associated traffic in astrocyte-related ALS pathogenesis.

Discussion
Here, we demonstrate the convergence of ALS pathways onto key processes related to dysregulated cellular transport involving KIF5A, a kinesin-1 heavy-chain subunit that has not been visualised in astrocytes previously. We show direct experimental evidence that KIF5A is widely distributed along astrocyte processes and its deficiency leads to disruption of structural integrity and mitochondrial transport, suggesting a loss of energy supply to distal process segments. This paradigm also implicates an underlying cause for cytoskeletal alterations in SOD1 ALS astrocytes characterised by impaired kinesin-1/KIF5A distribution as JNK1, a kinesin regulator, can restore the phenotype. Our multi-dimensional discovery pipeline pinpoints a key gene expression and pathway signature in ALS, implying a common pathogenic route. Previous efforts to integrate genetic associations and gene expression have primarily focused on expression quantitative trait loci (eQTL) analysis where single nucleotide polymorphisms (SNPs) linked to a disease via GWAS are also shown to be linked to changes in mRNA/protein levels 32 . A recent study also explored ALS pathway signatures based on polygenic score analysis, which were found to be enriched not only in neurons but also in glial cells 5 . However, combining these methods with biological validations in various cell populations, including non-neuronal cells, is a prerequisite for addressing the pathogenicity of genetic variants and their key effectors. This is highly relevant, given the limited availability of robust brain cell Plots illustrate the percentages of mitoB + /EAAT2+ area-over-laps in control and SOD1 ALS AC processes. n = 8 control and 8 SOD1 ALS ACs (5-6 images per AC from two experiments); Data represents mean ± SD. c Western blot (WB) images of EAAT2 immunoreactive bands in control and SOD1 ALS AC samples. Graph shows band densities normalised to control (1) and to β-actin bands. n = 3 independent batches of control and ALS AC cultures; data represent mean ± SEM. d Representative SR-SIM immunofluorescence images of non-or JNK1-GFP-transfected ALS AC processes, demonstrating a-tubulin + , EAAT2+ and mitoB-stained (mitochondria) objects. Insets (lower image panels) illustrate magnified areas indicated by the yellow boxes. e Graph illustrates area-overlaps between mitoB + /EAAT2+ immunoreactive objects in AC processes. n = 8 JNK1-GFP-and 10 JNK1-GFP+ ACs from two experiments (5-6 images per AC from two experiments); data expressed as mean ± SD. f WB images of COX IV and EAAT2 immunoreactive bands in GFP and JNK1-GFP vector transfected ALS AC samples. Graphs show band densities normalised to GFP (non-JNK1 + ) AC bands (1) and to b-actin bands. n = 3 independently transfected AC culture batches per group; data expressed as mean ± SEM. For each analysis, unpaired twotailed t-test was used. Scale bars: Scale bars: 2μm (1μm for insets). Please, also see Suppl. Fig. 10.
type-specific eQTL databases. The integrative approach shown in our work illustrates how network-expansion analyses can be exploited to integrate cell type-specific differential expression data with genetic evidence despite minimal overlap at the gene level, while discovering important pathological cellular pathways. We have identified five genes mapped within a protein interaction module that shows enrichment in ALS-related gene-network expansion elements and in astrocytic RNA/protein changes. Ontologies for four of these genes implicate involvement in intracellular transport. Amongst these, we focused on KIF5A for an in-depth study for multiple reasons: their presence and function have not been previously described in astrocytes; neuronal kinesin-1/KIF5A dysfunction has been implicated in a broad range of diseases caused by KIF5A-mutations, including ALS 15,16 , Charcot-Marie-Tooth type 2 disease (CMT2) and hereditary spastic paraplegia (HSP) 33,34 ; kinesin1/KIF5A-related perturbations can also occur in non-KIF5A mutation-related ALS forms 21,22 . Our integrated network-expansion study supports that kinesin-1/KIF5A-related pathway disturbances are more broadly shared and that mechanisms could vary across different ALS genotypes, including gain of toxic function or loss of function. For instance, KIF5A(Δexon27) mutant oligomers are hyperkinetic, aggregate, dysregulate transport [35][36][37] and cause similar SOD1 protein aggregations seen in the SOD1 D90A mutation 38 , leading to axonal cytoskeletal changes and cytotoxicity. In contrast, our SOD1 ALS mutation-related paradigm demonstrates loss of KIF5A function through decreased expression and protein levels. Due to the close functional coupling of astrocyte processes and synapses 39 , kinesin-1/KIF5A-related astrocytic abnormalities could worsen the breakdown of neuronal networks in ALS.
Interestingly, a genetic variation within another molecular motor, the kinesin-associated protein 3 gene was proposed as a disease-modifying factor associated with increased survival or with an upper MN-predominant phenotype in sporadic ALS 40,41 . However, no link was found in broader population-based studies 42,43 . This suggests complexity in human genetic studies evaluating genetic risks and influences, highlighting the importance of alternative integrative frameworks supported by validations in biological platforms.
In contrast to previous observations 18 , we show that KIF5A is expressed in astrocytes, albeit at low levels. The demonstration of astrocytic KIF5A content in CNS tissues and its alterations in ALS may have been hampered by difficulties in detecting low protein levels by immunohistochemical approaches. Although the use of SR-SIM is confined to low-throughput analyses, our mechanistic studies using this method allowed us to detect and track KIF5A+ particles despite ultra-low protein abundance in processes of cultured control and SOD1 ALS astrocytes. SR-SIM, along with morphological analyses, were also central to showing that low KIF5A levels lead to disturbances in MT architecture and process formation. It is likely to underlie the SOD1 ALS astrocyte phenotype as restored KIF5A expression rescues their morphology. This conclusion is supported by studies on neurons, demonstrating kinesin-dependent MT elongation 44 , organisation 45 and stability 25 , which influences cargo distribution in polarised cells.
The dramatic effect of KIF5A KD on astrocyte morphology, with unchanged protein levels of KIF5B and KIF5C subunits, suggests that KIF5A has a non-redundant function in certain cell types. It is supported by studies showing that KIF5A mutationrelated dysfunction remains uncompensated in diseases and its unique ability to affect mitochondrial localisation, which is essential in process maintenance 26 .
Aligned with the observed KIF5A-dependent MT and cytoarchitectural abnormalities, we found a decline in mitochondrial traffic and distribution in SOD1 ALS astrocyte processes. Our results suggest that this can be attributed to the diminished availability of KIF5A for anchoring mitochondria, leading to kinesin-1-related traffic impairment. In addition to the low KIF5A abundance, other plausible mechanisms may have also facilitated mitochondrial cargo issues in SOD1 astrocytes. For instance, the mutant SOD1 protein has been demonstrated to reduce mitochondrial transport in transfected cortical neurons by lowering Miro1 levels, a protein anchoring KIF5A 21 , and also potentially through P38 MAP kinase-dependent phosphorylation of kinesin-1 46 . Similar decoupling were also observed in C9ORF72 22 , VAPBP56S 47 , and FUS 48 ALS mutations, which broadens the relevance of KIF5A in ALS pathogenesis.
Our results, together with the above findings, highlight that targeting KIF5A could potentially improve kinesin-1-mediated cargo in astrocytes and neurons in ALS. Since we found that human astrocytes have low baseline KIF5A levels, it is plausible to speculate that even a small improvement in KIF5A protein levels or activity would amend astrocyte process morphology and function. Previous work indicated that JNK1, a kinesin-1/KIF5 transport regulator 49 , stimulates MT elongation in HeLa cells when MT arrangements are disrupted without affecting kinesin-1 levels 23 . Our study corroborated this observation and provided direct evidence that process-formation and mitochondrial transport can be improved in human SOD1 ALS astrocyte process tips by JNK1 overexpression. It seems to compensate for the loss of KIF5A availability, functionally. While more investigations are required, this suggests that JNK1 can enhance kinesin-1 motility and transport function in SOD1 ALS astrocytes, similarly to that seen for rodent neurons 49 . Although it remains to be explored whether pharmacological activation of JNK1 is a plausible approachespecially in light of emerging small-molecule kinase activators 28our findings demonstrate a proof-ofprinciple strategy for restoring astrocyte process structure and cargo distribution in ALS.
In our paradigm, astrocyte process and cargo disturbances represent a loss of function (LOF) phenotype, supporting the emerging LOF effects 12 in ALS, in addition to the well-described gain of toxic functions 11 . This phenotype may cause neuronal network dysfunction by reducing synapse coverage and support by impaired astrocyte process arborisation or perturbations to glutamate clearance. In particular, our findings implying diminished mitochondrial association with EAAT2 due to low astrocytic KIF5A levels suggest a mechanism underlying glutamate transport dysfunction 50 , in addition to downregulated EAAT2 levels, one of the earliest observed astrocyte-related molecular pathologies in ALS 31,51 . However, more functional assessments are required to explore this possibility further. In contrast to LOF, an integrative proteomics-based regulatory network study by Mishra and colleagues 52 have recently found that ligand-receptor interactions mediate a toxic gain of SOD1 ALS astrocyte function through releasing APP that exerts MN toxicity by DC6-signalling.
Both experimental examples highlight the need for advanced system biology approaches with the additional integration of human and mouse multi-omics studies to explore the full picture of loss or gain of function pathways in astrocytes, potentially guiding a multi-target strategy in ALS.
We propose that our pipeline is a powerful tool for identifying cell type-specific biological pathways, which led us to discover the presence of KIF5A in astrocytes and its regulatory role for process integrity and cargo regulation. Our work also indicates that modest improvements in kinesin-1/KIF5A-dependent transport may improve astrocyte process-mediated support of neuronal networks with relevance to ALS therapeutics.

Methods
Human interactome and network-based expansion. For the human interactome, we selected STRING v11.0 (combined score > 0.75), BioGRID (v 3.5.172) and IntAct (May 2019) databases derived from high-throughput studies (reporting at least 1,000 interactions). All nodes were mapped to Ensembl gene (ENSG) identifiers, edges were considered as non-directed connections and self-loops and duplicated edges were eliminated from the interactome. For the network-based expansion approach, the input hits were mapped to the interactome and their initial weights were diffused using Personalised PageRank (PPR) algorithm from the igraph R package (https://igraph.org/r/). The nodes receiving the 25% upper part of the PageRank ranking score were selected for community detection, using walktrap clustering in igraph. Resulting communities with more than 300 nodes were subjected to further rounds of clustering until threshold values were reached. Communities with >10 nodes were defined as modulesand as significant modules if their PageRank score was calculated as significant (adjusted p value < 0.05) by Kolmogorov Smirnov test followed by Benjamini-Hochberg post hoc analysis.
GWAS Catalogue of neurodegenerative diseases. We selected nine neurodegenerative diseases from the parent term EFO:0005772 for neurodegenerative disease in the Experimental Factor Ontology (EFO) database (https://www.ebi.ac. uk/efo/), which had at least two genes mapped to significant SNPs in the GWAS Catalogue (https://www.ebi.ac.uk/gwas/). The -log10 (p value) was used as starting weight for network expansions, selecting the highest value when redundancies were present. The ALS (EFO_0000253, replaced by MONDO_0004976) and sporadic ALS (EFO_0001357) databases were pooled. For bulk Jaccard index calculations (Fig. 1c), all genes found in significant modules were selected for each disease. The same principles were applied to the analysis in Fig. 1d, but only pairs with a ≥ 0.7 Jaccard index were chosen for further analyses, resulting in 10 groups of overlapping modules. For each group of overlapping modules, Gene Ontology Biological Process (GOBP) enrichment (Fisher test with Benjamini-Hochberg post hoc analysis) was expressed as moduler identifier.
Multi-omics data integration. For multidimensional data integration, the filtered GWAS dataset was combined with astrocyte-related transcriptomic and proteomic datasets that were extracted from published studies 14 and databases (accession code: GSE102903). The proteomics dataset was further analysed using the Perseus software (https://maxquant.net/perseus/), and the significance A value was calculated from averaged ratios. For network expansion, the -log10 of the reported p values was used as a starting signal for each set. Module overlap significance was defined by the Jaccard index. In addition, to further characterise the triple overlaps (GWAS/transcriptomics/proteomics), the GOBP enrichment analysis was performed using the Fisher test with Benjamini-Hochberg correction.
PageRank score benchmarking. Three groups were revealed as True Positive (TP) gene-disease relations by the following steps. From the DISEASE database, we extracted all genes linked to three neurodegenerative diseases: ALS, AD and PD. Next, we selected all associations that were greater than the third quantile (25% most significant hit: sco>Q3) and the ones with the maximum score (max), which resulted in two groups. Finally, we extracted drug targets from the ChEMBL database assembled using findings by Phase II and phase IV clinical trials for ALS, AD and PD. Areas under the receiver operating characteristic (ROC) curves were calculated using the pROC R package. Human astrocyte differentiation. Astrocyte differentiation was performed using a published protocol 14,53 . In brief, human iPSCs were used to generate spinal neural stem cells (NSCs) which were expanded for~100 days. Expansion was carried out in N2B27 media containing 10 ng/ml FGF-2, and cells were passaged using Accutase at 70% confluency. Then two subsequent rounds of cell sorting were carried out using GLAST antibody-tagged magnetic beads (Miltenyi Biotec, 130-095-825) to obtain pure glial cell progenitor cultures (GPCs), according to the manufacturer's protocol. GPCs were frozen and banked before being differentiated into astrocytes by supplementing N2B27 culture media with BMP-4 (Peprotech, 120-05) and hLIF (Peprotech, 300-05), both at 10 ng/ml, for four weeks.
Mouse astrocyte cultures. Mice were housed and used in accordance with the Home Office Animal Scientific Procedures Act (ASPA), 1986 under project licence P98A03BF9. The upper cervical spinal cord-lower brainstem tissue was dissected from newborn (P0) male and female mice (n = 6 mice/each culture; C57BL/6 J, RRID: IMSR_JAX:000664 l; provided by Dr Andrea Loreto). The tissue was physically dissociated into small pieces, followed by enzymatic digestion with 0.25% trypsin and 0.2% collagenase in a mechanical dissociator (Gentle MACS Octo dissociator, Milteny Biotec) at 37°C for 30 minutes. The obtained single cell suspension was plated in poly-D-Lysine coated T75 flasks and kept in DMEM supplemented with 10% foetal bovine serum (FBS) and 1% antibiotic-antimycotic mix (Thermo Fisher Scientific, 15240096). At day 7, top dwelling progenitor cells and microglia were removed by overnight shaking at 100 rpm; then the culture was differentially passaged using 0.0025% trypsin to remove microglia. Finally, 0.025% trypsin was applied for 2-3 minutes to detach astrocytes for replating, while fibroblast mostly remained attached. Further purification was carried out by GLAST antibody-tagged magnetic beads by published methods 14 , which resulted in high-purity astrocyte cultures ( > 98%).
Western blots. Cell lysates for protein samples were isolated from astrocyte cultures at 50-60DIV or from HEK293 cells 48 h after transfection, using the RIPA lysis buffer (Sigma-Aldrich, #R0278) supplemented with Pierce Protease Inhibitor Mini Tablets (Thermo Fisher Scientific, #31462) and Pierce Phosphatase Inhibitor Mini Tablets (Thermo Fisher Scientific, #A32957). For astrocytes, 14μg of protein and for HEK293 cells, 20 μg of protein was resolved by SDS-PAGE and then transferred onto PVDF membranes. Then, membranes were incubated overnight with primary antibodies, except for the directly HRP-tagged β-actin antibody (1hour incubation) used as a loading control. HRP-tagged secondary antibodies were applied for 1 h at room temperature (RT), and the blots were developed using the enhanced chemiluminescence system (GE Healthcare, #RPN2232).
Immunocytochemistry. Cells on coverslips were washed once with PBS and subsequently fixed with 4% PFA (Thermo Fisher Scientific, 28908) for 12 minutes. Fixed cells were washed with PBS three times, followed by blocking with 0.3% Triton-X (Sigma, T8787) containing 10% normal goat serum (NGS -Sigma, G9023) for 1 hour. Then cells were incubated with the respective primary antibodies in 0.1% Triton-X containing 3% NGS for 2 hours at RT. Secondary antibody incubation was performed for 1 hour at RT. Phalloidin was applied together with the secondary antibodies. For nuclear labelling, cells were incubated with DAPI (Sigma, D9542) in PBS for 10 minutes at RT. Coverslips were mounted on slides using FluorSave (VWR, 345789) for conventional confocal microscopy or Prolong gold (Molecular Probes, P10144) for SR-SIM. The list of antibodies and their details are provided in Supplementary Table 2.
Immunohistochemistry. For human spinal cord tissue sections, 7 μm paraffinembedded sections were subjected to deparaffinisation, rehydration and extended microwave antigen retrieval in citrate buffer, followed by blocking in 10% normal goat serum. Primary antibody solutions were added to the slides for 1 hour at 37°C. After washes in PBS, slides were incubated for 45 minutes at RT with secondary antibodies. The slides were mounted with coverslips using Vectashield with DAPI (Vector Laboratories).
Live mitochondrium tracking. Human or mouse primary astrocytes were plated on either geltrex-coated 8-well chambers (Ibidi, 80826) or on CELLview glassbottom Petri dishes (Greiner Bio One, 627860). Cells were incubated in 500 nM MitoBright Red (a membrane potential independent dye; Dojindo, MT11; gift from Dr András Füredi) diluted in culture media and incubated for 30 min at 37°C. Media was changed before imaging to Neurobasal without phenol and with 1% Glutamax. Images were taken by a spinning-disc confocal microscope (Olympus IX70 microscope, Hamamatsu ORCA-ER CCD camera, PerkinElmer UltraVIEW scanner controlled by the MetaMorph software) at 1 second intervals. For tracking mitochondrial movements, the TrackMate ImageJ plugin was used.
Cell shape analysis. The form factor (FF) was then used to compare cell shape/ process arborisation affected by decreased expression of KIF5A in SOD1 ALS astrocytes and in siRNA-treated control astrocytes, and in rescue experiments using KIF5A-mScarlet or JNK1-GFP expression plasmids. FF is defined by the following equation: FF = 4π[area]/[perimeter] 2. , providing a value between 0 and 1. Values closer to 0 represent a polarised cell shape (long and multiple processes), and values closer to 1 indicate circular cell shape (loss of processes). The area and perimeter measurements in ImageJ were based on the visualisation of mScarlet, GFP or F-actin fluorescence by staining astrocytes that were identified by GFAP or ALDH1L1 immunoreactivity and tRFP (Evrogen, AB233), GFP (Proteintech, 50430-2-AP) or phalloidin-488 (Thermo Scientific, A12379) labelling, respectively.
Super-resolution structured illumination microscopy (SR-SIM) analysis. For SR-SIM, a Zeiss Elyra PS1 (Carl Zeiss GmbH) was used with a 63×1.4NA Zeiss Plan-Apochromat objective, based on a published protocol 54 . Briefly, prior to imaging, fixed and immunostained cells were mounted with 20μl ProLong Gold (P36934, LifeTechnologies) on high-precision 18mm 2 coverslips (Zeiss Ltd, 170 μm thickness) and allowed to stand at 21-23°C for 72 hours. The slides were preheated to 23°C to match the immersion oil temperature in the imaging chamber. Microscopy was performed by the recommended parameters (excitation: 488 nm/ 200 mW, 560 nm/200 mW and 640 nm/150 mW; structured illumination grid pitches: 28 μm, 34 μm and 34 μm; emission filters: BP495-550, BP570-620,LP655nm). Z-stack through-focus series of images (5)(6)(7)(8)(9)(10)(11)(12) were taken at a spacing interval of 0.091 μm in 3 colours sequentially (5 grid phase shifts/rotations). A z-stack with 10 slices covered~1 μm thickness of the specimen. Image acquisition was performed by a PCO edge sCMOS device (PCO AG, Kelheim, Germany) with a sensor allowing up to 2240×2154 pixel images. The raw data from the multi-dimensional image arrays were processed by the built-in ZEN software. Then, channel alignment was performed, which was based on the calibration data obtained earlier using bead samples, and the output images were saved as.czi files. The microtubule dispersion factor (MDF) was used to compare more subtle changes in the astrocyte process structures between the control and SOD1 ALS group. The measurements for MDF were carried out in astrocyte processes (cytoplasmic protrusions) immunostained for α-tubulin, using the directionalityplugin in ImageJ. Then, the Fourier Components method was applied to build orientation maps in the same plugin. KIF5A immunoreactive particles were visualised by SR-SIM to analyse KIF5A protein distribution and level changes in astrocyte processes. KIF5A+ object segmentation was performed in CellProfiler (v4.2.1; IdentifyObjects) and the distance of each KIF5A+ particle from the process tip was measured using the analyse particle plugin in ImageJ, following equal thresholding across all images. KIF5A+ particle density (n of KIF5A+ particles within each 1 μm segment of process volume across the distal 15 μm length) was quantified in each process. Data were expressed as average for each distance/ volume bin (1-15 μm) for comparing particle distribution in distal astrocyte processes between different experimental groups. For mitochondria-EAAT2 immunoreactive area-overlap analysis in double-stained astrocytes, the images were manually thresholded for MitoBright Red fluorescence to accurately identify mitochondria-occupied areas in astrocyte processes. This area was selected in the create selection plugin in ImageJ, and the percentages of overlapping EAAT-2+ territories were measured.
Image processing and analysis. For immunofluorescence, images were taken either by a fluorescent (Leica DM6000, ×20-63 objectives), confocal (Leica TCS SPE and Nikon Upright Ni-E/A1R) or a super-resolution microscope (for SR-SIM see details above). Camera gain and exposure were kept unchanged while collecting images. For unbiased semi-automated analyses of KIF5A particle and mitochondria distribution or density, unmodified images were uniformly thresholded for the Analyse Particles function or area/volume measurements in ImageJ (v.2.0.0 Fiji) within each experiment. Cell counts were performed manually. Western blot (WB) membrane chemiluminescence was imaged, using the Alliance 4.7 CCD Image System (UVITEC). Quantitative WB analyses were performed in ImageJ, following published standard methods 55 . Band density levels were expressed as fold-changes to controls following normalisation to corresponding β-actin+ bands and to a control sample within the same blot. For micrograph illustrations, the recommended guidelines were followed. Images were minimally processed in ImageJ or Adobe Photoshop (v21.0.3), which was applied equally to samples that were directly compared without compromising data presentation. The processing included uniform changes in gain parameters when clear views were obscured in merged panels. Pseudo-colours in images were rendered in ImageJ for multicolour visualisation in images. For WB illustrations, images were cropped for focussed visualisation, leaving a 6-band width. All uncropped WB images were included in the Supplementary Information. For figure assembly, images were embedded and organised in Adobe Illustrator (v24.0.3). Schematic images of mice were used in the figures, which complies with the content license of pixabay, a royalty-free contentsharing platform.
Statistics and reproducibility. Statistical test details and exact sample sizes are listed in Supplementary Table 3. Briefly, subject identifiers were blinded for the observers throughout this study. At least three independent biological repeats (independent culture batches/transfections, postmortem patient samples) were used in repeated mechanistic experimental studies, including at least three technical replicates (histological sections, astrocytes). For low-throughput but detailed SR-SIM-based analyses, data acquisition involved multiple astrocyte processes in two or three repeated experiments, and the variance of data across astrocytes or cultures was compared between groups. The GraphPad Prism v.8.0 software was used for statistical analyses and data plotting. When normality was not assumed, nonparametric tests were used. The type of tests, with exact n values and p values were indicated in the figures and legends with further detail provided in Supplementary Table 3. Statistical significance was accepted at p < 0.05.
Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
Data that support the results in this work are provided in the Supplementary Data 1-7 files and in the Supplementary Figures 1-11, including the uncropped WB images (Supplementary Information) or are available from the corresponding authors upon reasonable request. The GWAS Catalogue is available at https://www.ebi.ac.uk/gwas/ and the transcriptomic database used is available from GEO (Accession code: GSE102903).